Probabilistic fuzzy logic framework in reinforcement learning for decision making

Abstract

This dissertation focuses on the problem of uncertainty handling during learning
by agents dealing in stochastic environments by means of reinforcement learning.
Most previous investigations in reinforcement learning have proposed algorithms
to deal with the learning performance issues but neglecting the uncertainty present
in stochastic environments.
Reinforcement learning is a valuable learning method when a system requires a
selection of actions whose consequences emerge over long periods for which
input-output data are not available. In most combinations of fuzzy systems with
reinforcement learning, the environment is considered deterministic. However, for
many cases, the consequence of an action may be uncertain or stochastic in nature.
This work proposes a novel reinforcement learning approach combined with the
universal function approximation capability of fuzzy systems within a
probabilistic fuzzy logic theory framework, where the information from the
environment is not interpreted in a deterministic way as in classic approaches but
rather, in a statistical way that considers a probability distribution of long term
consequences.
The generalized probabilistic fuzzy reinforcement learning (GPFRL) method,
presented in this dissertation, is a modified version of the actor-critic learning
architecture where the learning is enhanced by the introduction of a probability
measure into the learning structure where an incremental gradient descent weight-
updating algorithm provides convergence.
XXIABSTRACT
Experiments were performed on simulated and real environments based on a
travel planning spoken dialogue system. Experimental results provided evidence
to support the following claims: first, the GPFRL have shown a robust
performance when used in control optimization tasks. Second, its learning speed
outperforms most of other similar methods. Third, GPFRL agents are feasible and
promising for the design of adaptive behaviour robotics systems.